Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00100
A. K. Panda, B. Kosko
A system of rules can approximate a trained neural classifier after sampling from that classifier. The rules define a generalized probability mixture that then describes the classifier. The size or granularity of the rule if-parts defines a foam-like structure with a few large rule if-part set bubbles in patternclass centers and many smaller if-part sets near class borders. The rule foam's mixture gives a Bayesian posterior over the rules. The posterior describes the relative importance of each rule for each observed input and output. The foam's mixture also gives the conditional variance that measures the uncertainty in its output. So the rule base is statistically interpretable as well as modular and adaptive. A rule foam with 1000 Gaussian rules approximated a 96.85% accurate MNIST neural classifier and had itself 95.66% classification accuracy. Foams can also approximate other foams. Some approximator foams out-performed the target foam that generated their training data. The rule foam's granularity mitigates the rule explosion inherent in the rule-based approximator's graph-covering structure
{"title":"Converting Neural Networks to Rule Foam","authors":"A. K. Panda, B. Kosko","doi":"10.1109/CSCI49370.2019.00100","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00100","url":null,"abstract":"A system of rules can approximate a trained neural classifier after sampling from that classifier. The rules define a generalized probability mixture that then describes the classifier. The size or granularity of the rule if-parts defines a foam-like structure with a few large rule if-part set bubbles in patternclass centers and many smaller if-part sets near class borders. The rule foam's mixture gives a Bayesian posterior over the rules. The posterior describes the relative importance of each rule for each observed input and output. The foam's mixture also gives the conditional variance that measures the uncertainty in its output. So the rule base is statistically interpretable as well as modular and adaptive. A rule foam with 1000 Gaussian rules approximated a 96.85% accurate MNIST neural classifier and had itself 95.66% classification accuracy. Foams can also approximate other foams. Some approximator foams out-performed the target foam that generated their training data. The rule foam's granularity mitigates the rule explosion inherent in the rule-based approximator's graph-covering structure","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134241878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00158
C. V. Gonzalez, Gwang Jung
To handle cyber security threats, we need to develop courses to educate students about cyber security concepts, methods to handle various attacks in the cyber space. In this paper, we address what resources would be required to develop courses to effectively teach students the cyber security concepts and methods at small colleges or universities with limited resources.
{"title":"Teaching Cyber Security Topics Effectively in a College or University with Limited Resources","authors":"C. V. Gonzalez, Gwang Jung","doi":"10.1109/CSCI49370.2019.00158","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00158","url":null,"abstract":"To handle cyber security threats, we need to develop courses to educate students about cyber security concepts, methods to handle various attacks in the cyber space. In this paper, we address what resources would be required to develop courses to effectively teach students the cyber security concepts and methods at small colleges or universities with limited resources.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131626956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00014
Abdulwahab Alazeb, B. Panda
The evolution of the utilization of technologies in nearly all aspects of life has produced an enormous amount of data essential in a smart city. Therefore, maximizing the benefits of technologies such as cloud computing, fog computing, and the Internet of things is important to manage and manipulate data in smart cities. However, certain types of data are sensitive and risky and may be infiltrated by malicious attacks. As a result, such data may be corrupted, thereby causing concern. The damage inflicted by an attacker on a set of data can spread through an entire database. Valid transactions that have read corrupted data can update other data items based on the values read. In this study, we introduce a unique model that uses fog computing in smart cities to manage utility service companies and consumer data. We also propose a novel technique to assess damage to data caused by an attack. Thus, original data can be recovered, and a database can be returned to its consistent state as no attacking has occurred.
{"title":"Maintaining Data Integrity in Fog Computing Based Critical Infrastructure Systems","authors":"Abdulwahab Alazeb, B. Panda","doi":"10.1109/CSCI49370.2019.00014","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00014","url":null,"abstract":"The evolution of the utilization of technologies in nearly all aspects of life has produced an enormous amount of data essential in a smart city. Therefore, maximizing the benefits of technologies such as cloud computing, fog computing, and the Internet of things is important to manage and manipulate data in smart cities. However, certain types of data are sensitive and risky and may be infiltrated by malicious attacks. As a result, such data may be corrupted, thereby causing concern. The damage inflicted by an attacker on a set of data can spread through an entire database. Valid transactions that have read corrupted data can update other data items based on the values read. In this study, we introduce a unique model that uses fog computing in smart cities to manage utility service companies and consumer data. We also propose a novel technique to assess damage to data caused by an attack. Thus, original data can be recovered, and a database can be returned to its consistent state as no attacking has occurred.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132764602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00264
Abeer Abdel Khaleq, Ilkyeun Ra
Cloud applications are becoming more containerized in nature. Developing a cloud application based on a microservice architecture imposes different challenges including scalability at the container level. What adds to the challenge is that applications have different QoS requirements and different characteristics requiring a customized scaling approach. In this paper, we present an agnostic approach algorithm for microservices autoscaling deployed on the Google Kubernetes Engine. Our algorithm adapts the Kubernetes autoscaling paradigm based on the application characteristics and resource requirements. Initial testing of the algorithm on different microservices requirements show an enhancement in the microservice response time up to 20% compared to the default autoscaling paradigm.
{"title":"Agnostic Approach for Microservices Autoscaling in Cloud Applications","authors":"Abeer Abdel Khaleq, Ilkyeun Ra","doi":"10.1109/CSCI49370.2019.00264","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00264","url":null,"abstract":"Cloud applications are becoming more containerized in nature. Developing a cloud application based on a microservice architecture imposes different challenges including scalability at the container level. What adds to the challenge is that applications have different QoS requirements and different characteristics requiring a customized scaling approach. In this paper, we present an agnostic approach algorithm for microservices autoscaling deployed on the Google Kubernetes Engine. Our algorithm adapts the Kubernetes autoscaling paradigm based on the application characteristics and resource requirements. Initial testing of the algorithm on different microservices requirements show an enhancement in the microservice response time up to 20% compared to the default autoscaling paradigm.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"253 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132842326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00147
Sahar Voghoei, Navid Hashemi Tonekaboni, D. Yazdansepas, H. Arabnia
It has been generally believed that higher participation in discussion forums in online classes would result in better student performance. To better understand this correlation on a large scale, we have studied 291 distinct online courses offered during Summer 2019 at Georgia Gwinnett College. Several studies in the literature have focused on analyzing the data from the Massive Open Online Courses (MOOCs). However, in this research, we have focused on University-based Online Courses (UOCs) for undergraduate students, where the curriculum enforces students to take these courses. Although a higher participation rate in online forums has a direct correlation with a higher grade in MOOCs, in OUCs, students with top grades are not necessarily the most active students. Our analysis shows a consistent pattern in UOCs where during the first two-thirds of the semester, students who belong to the GPA range of ~70 to ~80 percentile of the class have the highest rate of participation, while during the last one-third of the semester, the ones who belong to the GPA range of ~87 to ~93 percentile, contribute the most. On the other hand, we found out that the common characteristic of top students in all classes, is their consistency in participation throughout the semester, regardless of the number of their posts.
{"title":"University Online Courses: Correlation between Students' Participation Rate and Academic Performance","authors":"Sahar Voghoei, Navid Hashemi Tonekaboni, D. Yazdansepas, H. Arabnia","doi":"10.1109/CSCI49370.2019.00147","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00147","url":null,"abstract":"It has been generally believed that higher participation in discussion forums in online classes would result in better student performance. To better understand this correlation on a large scale, we have studied 291 distinct online courses offered during Summer 2019 at Georgia Gwinnett College. Several studies in the literature have focused on analyzing the data from the Massive Open Online Courses (MOOCs). However, in this research, we have focused on University-based Online Courses (UOCs) for undergraduate students, where the curriculum enforces students to take these courses. Although a higher participation rate in online forums has a direct correlation with a higher grade in MOOCs, in OUCs, students with top grades are not necessarily the most active students. Our analysis shows a consistent pattern in UOCs where during the first two-thirds of the semester, students who belong to the GPA range of ~70 to ~80 percentile of the class have the highest rate of participation, while during the last one-third of the semester, the ones who belong to the GPA range of ~87 to ~93 percentile, contribute the most. On the other hand, we found out that the common characteristic of top students in all classes, is their consistency in participation throughout the semester, regardless of the number of their posts.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117260599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00216
L. Alhazzaa, Anneliese Amschler Andrews
In any software development organization, reliability is crucial. Defect prediction is key in providing management with the tools for release planning. To predict defects we ask the question of how much data is required to make usable predictions? When testing, a rule of thumb is to start defect prediction after 60% of system test has been accomplished. In an operational phase, managers cannot usually determine what constitutes 60% of a release and might not want to wait that long to start defect prediction. Here we discuss the trade-offs between the need of early predictions versus making more accurate predictions.
{"title":"Trade-Offs between Early Software Defect Prediction versus Prediction Accuracy","authors":"L. Alhazzaa, Anneliese Amschler Andrews","doi":"10.1109/CSCI49370.2019.00216","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00216","url":null,"abstract":"In any software development organization, reliability is crucial. Defect prediction is key in providing management with the tools for release planning. To predict defects we ask the question of how much data is required to make usable predictions? When testing, a rule of thumb is to start defect prediction after 60% of system test has been accomplished. In an operational phase, managers cannot usually determine what constitutes 60% of a release and might not want to wait that long to start defect prediction. Here we discuss the trade-offs between the need of early predictions versus making more accurate predictions.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121050000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00044
Tobias Eggendorfer, Volker Eiseler
Tactical Data Links (TDL) and Computer Science meet usually when it comes to interoperability andimplementation. However looking at it from an IT security perspective, some interesting issues occur. These become more relevant the more military hard-and software is built using commercial of the shelf (COTS) systems, that are usually implemented using standard Internet technology and software development patterns. This paper looks at Link 16, Link 11 and VMF security considerations and how compatible they are to current IT security standards. Typical security issues are discussed and concepts to mitigate them presented, which however need to be analysed for their suitability to TDL.
{"title":"On the Relevance of IT Security in TDL","authors":"Tobias Eggendorfer, Volker Eiseler","doi":"10.1109/CSCI49370.2019.00044","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00044","url":null,"abstract":"Tactical Data Links (TDL) and Computer Science meet usually when it comes to interoperability andimplementation. However looking at it from an IT security perspective, some interesting issues occur. These become more relevant the more military hard-and software is built using commercial of the shelf (COTS) systems, that are usually implemented using standard Internet technology and software development patterns. This paper looks at Link 16, Link 11 and VMF security considerations and how compatible they are to current IT security standards. Typical security issues are discussed and concepts to mitigate them presented, which however need to be analysed for their suitability to TDL.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116987576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00096
E. Espinosa-Juárez, Jorge Luis Solano-Gallegos, F. Ornelas‐Tellez
This paper presents the problem of economic dispatch for an electrical system with unconventional energy sources and energy storage. The economic dispatch is considered for demand variations over 24 hours, taking into account the forecast of solar energy for one hour ahead, based on the autoregressive process. The implemented algorithm allows analysis of economic dispatch under different restrictions. A case study is shown, where different levels of renewable energy penetration into the system are considered and the effectiveness of the implemented algorithm is observed
{"title":"Economic Dispatch for Power System with Short-Term Solar Power Forecast","authors":"E. Espinosa-Juárez, Jorge Luis Solano-Gallegos, F. Ornelas‐Tellez","doi":"10.1109/CSCI49370.2019.00096","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00096","url":null,"abstract":"This paper presents the problem of economic dispatch for an electrical system with unconventional energy sources and energy storage. The economic dispatch is considered for demand variations over 24 hours, taking into account the forecast of solar energy for one hour ahead, based on the autoregressive process. The implemented algorithm allows analysis of economic dispatch under different restrictions. A case study is shown, where different levels of renewable energy penetration into the system are considered and the effectiveness of the implemented algorithm is observed","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115585147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00243
B. Sy, Jin Chen, Rebecca Horowitz
The goal of this research is to develop a predictive analytics technique based on manifold clustering of mixed data type. In this research, we explore the concept of statistically significant association patterns to induce an initial partition on data for deriving manifolds. Manifolds are hyperplanes embedded in low dimensions. The advantage of this novel technique is a bootstrap on data clusters that reveals statistical associations from the information-theoretic perspective. As an illustration, the proposed technique is applied to a real data set of diabetes patients. An assessment on the proposed technique is performed to investigate the effect of bootstrap based on association patterns. Results of the preliminary study demonstrate the feasibility of applying the proposed technique to real-world data.
{"title":"Incorporating Association Patterns into Manifold Clustering for Enabling Predictive Analytics","authors":"B. Sy, Jin Chen, Rebecca Horowitz","doi":"10.1109/CSCI49370.2019.00243","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00243","url":null,"abstract":"The goal of this research is to develop a predictive analytics technique based on manifold clustering of mixed data type. In this research, we explore the concept of statistically significant association patterns to induce an initial partition on data for deriving manifolds. Manifolds are hyperplanes embedded in low dimensions. The advantage of this novel technique is a bootstrap on data clusters that reveals statistical associations from the information-theoretic perspective. As an illustration, the proposed technique is applied to a real data set of diabetes patients. An assessment on the proposed technique is performed to investigate the effect of bootstrap based on association patterns. Results of the preliminary study demonstrate the feasibility of applying the proposed technique to real-world data.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115025086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00063
Ratheesh Ravindran, M. Santora, M. Faied, Mohammad Fanaei
One of the most crucial enabling technologies for automated driving systems is the ability to reliably detect and classify a wide range of traffic signs in various driving conditions at different distances. Due to the complexity and dynamic nature of driving environments, it is difficult to reliably detect traffic signs with conventional image processing methods. Artificial intelligence in combination with image processing has proven to be a great success to address this problem in recent studies. This paper focuses on the selection of Deep Neural Networks (DNN) based on the application-oriented performance by taking into consideration the mean Average Precision (mAP) and Frames Per Second (FPS) as the major evaluation criteria. Faster Region-based Convolutional Neural Network (Faster R-CNN) is a newly proposed DNN in the literature that has proven to exhibit a balanced tradeoff between mAP and FPS performance measures. This paper starts with a DNN transfer learning and then implements the Faster R-CNN algorithm for the real-time detection and classification of traffic signs using the Robot Operating System (ROS). To reduce the errors due to DNN inaccurate detection, Tesseract" is added to detect the text in the identified traffic signs. The German Traffic Sign Detection Benchmark (GTSDB) dataset is used in this paper, and additional dataset are created to solve the lack of certain traffic signs in the GTSDB dataset. Simulation with ROS-Gazebo and real-time trials using the Polaris Gem e2 equipped with NVIDIA Drive PX2 demonstrate the efficiency of the proposed integration of DNN with Tesseract in detecting and classifying a wide range of traffic signs.
{"title":"Traffic Sign Identification Using Deep Learning","authors":"Ratheesh Ravindran, M. Santora, M. Faied, Mohammad Fanaei","doi":"10.1109/CSCI49370.2019.00063","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00063","url":null,"abstract":"One of the most crucial enabling technologies for automated driving systems is the ability to reliably detect and classify a wide range of traffic signs in various driving conditions at different distances. Due to the complexity and dynamic nature of driving environments, it is difficult to reliably detect traffic signs with conventional image processing methods. Artificial intelligence in combination with image processing has proven to be a great success to address this problem in recent studies. This paper focuses on the selection of Deep Neural Networks (DNN) based on the application-oriented performance by taking into consideration the mean Average Precision (mAP) and Frames Per Second (FPS) as the major evaluation criteria. Faster Region-based Convolutional Neural Network (Faster R-CNN) is a newly proposed DNN in the literature that has proven to exhibit a balanced tradeoff between mAP and FPS performance measures. This paper starts with a DNN transfer learning and then implements the Faster R-CNN algorithm for the real-time detection and classification of traffic signs using the Robot Operating System (ROS). To reduce the errors due to DNN inaccurate detection, Tesseract\" is added to detect the text in the identified traffic signs. The German Traffic Sign Detection Benchmark (GTSDB) dataset is used in this paper, and additional dataset are created to solve the lack of certain traffic signs in the GTSDB dataset. Simulation with ROS-Gazebo and real-time trials using the Polaris Gem e2 equipped with NVIDIA Drive PX2 demonstrate the efficiency of the proposed integration of DNN with Tesseract in detecting and classifying a wide range of traffic signs.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115574183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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